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<table width="100%" summary="page for meteo"><tr><td>meteo</td><td align="right">R Documentation</td></tr></table>

<h2> Meteorological Measurements for 11 Years </h2>

<h3>Description</h3>


<p>Several meteorological measurements for a period between 1920 and 1931.
</p>


<h3>Usage</h3>

<pre>data("meteo")</pre>


<h3>Format</h3>


<p>A data frame with 11 observations on the following 6 variables.
</p>

<dl>
<dt><code>year</code></dt><dd><p>the years.</p>
</dd>
<dt><code>rainNovDec</code></dt><dd><p>rainfall in November and December (mm).</p>
</dd>
<dt><code>temp</code></dt><dd><p>average July temperature.</p>
</dd>
<dt><code>rainJuly</code></dt><dd><p>rainfall in July (mm).</p>
</dd>
<dt><code>radiation</code></dt><dd><p>radiation in July (millilitres of alcohol).</p>
</dd>
<dt><code>yield</code></dt><dd><p>average harvest yield (quintals per hectare).</p>
</dd>
</dl>



<h3>Details</h3>


<p>Carry out a principal components analysis of both the covariance
matrix and the correlation matrix of the data and compare the
results. Which set of components leads to the most meaningful
interpretation? 
</p>


<h3>Source</h3>


<p>B. S. Everitt and G. Dunn (2001), <EM>Applied Multivariate Data
Analysis</EM>, 2nd edition, Arnold, London.
</p>


<h3>Examples</h3>

<pre>

  data("meteo", package = "HSAUR")
  meteo

</pre>


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